Live Traffic English Text Monitoring Using Fuzzy Approach

Current communication systems are very efficient and being used conveniently for secure exchange of vital information. These communication systems may be misused by adversaries and antisocial elements by capturing our vital information. Mostly, the information is being transmitted in the form of plain English text apart from securing it by encryption. To avoid losses due to leakage of vital information, one should not transmit his vital information in plain form. For monitoring of huge traffic, we require an efficient plain English text identifier. The identification of short messages in which words are written in short by ignoring some letters as in mobile messages is also required to monitor. We propose an efficient plain English text identifier based on Fuzzy measures utilizing percentage frequencies of most frequent letters and least frequent letters as features and triangular Fuzzy membership function. Presented method identifies plain English text correctly even, the given text is decimated/discontinuous and its length is very short, and seems very useful.

[1]  C. E. Veni Madhavan,et al.  Steganography based Information Security , 2002 .

[2]  Shalini Puri,et al.  A technical study and analysis on fuzzy similarity based models for text classification , 2012, ArXiv.

[3]  Sankar K. Pal,et al.  Fuzzy models for pattern recognition : methods that search for structures in data , 1992 .

[4]  Anto Satriyo Nugroho,et al.  Text Classification Techniques Used to Faciliate Cyber Terrorism Investigation , 2010, 2010 Second International Conference on Advances in Computing, Control, and Telecommunication Technologies.

[5]  Solomon Kullback,et al.  Statistical Methods in Cryptanalysis , 1976 .

[6]  Fabrizio Sebastiani,et al.  Machine learning in automated text categorization , 2001, CSUR.

[7]  William Stallings,et al.  Cryptography and network security , 1998 .

[8]  Didier Dubois,et al.  Fuzzy sets and systems ' . Theory and applications , 2007 .

[9]  Sankar K. Pal,et al.  Fuzzy models for pattern recognition , 1992 .

[10]  W. B. Cavnar,et al.  N-gram-based text categorization , 1994 .

[11]  Upasana Pandey,et al.  A Survey on Text Classification Techniques for E-mail Filtering , 2010, 2010 Second International Conference on Machine Learning and Computing.

[12]  Stephan Katzenbeisser,et al.  Information Hiding Techniques for Steganography and Digital Watermaking , 1999 .

[13]  A.K.S. Wong,et al.  Using complex linguistic features in context-sensitive text classification techniques , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[14]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[15]  Alfred Menezes,et al.  Handbook of Applied Cryptography , 2018 .

[16]  Stephen G. Wilson,et al.  Digital Modulation and Coding , 1995 .

[17]  Pratibha Yadav,et al.  Index of Garbledness for Automatic Recognition of Plain English Texts , 2010 .